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A double-Q network collaborative multi-objective optimization algorithm for precast scheduling with curing constraints
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DOI：10.1016/j.swevo.2024.101619.png)
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摘要
En 中文
Precast scheduling is a special case of optimization problem, in which the curing stage requires parallel processing of a batch of jobs. In this study, distributed flexible job shop scheduling with crane transportation, fuzzy processing, and curing constraints is modeled for precast scheduling. Three objectives are considered simultaneously, namely, fuzzy makespan, energy consumption, and economic cost. To solve this complex problem, a bilevel collaborative multi-objective optimization evolution algorithm (BCMOEA) is developed. First, a double-Q network is designed considering different groups of features to generate valuable solutions for the secondlevel components. Then, a dynamic-adjusted reference point set is embedded to divide the current population into three sub-populations. Furthermore, a collaboration mechanism is developed to learn different knowledge from these sub-populations to balance the convergence and diversity abilities. Moreover, a knowledge driven curing batching heuristic is designed to improve the fuzzy makespan. Finally, a set of instances generated based on the realistic precast process is tested, and detailed comparisons with the state-of-the-art algorithms show the competitive performances of the proposed algorithm.
Keyword:
Scheduling
Fuzzy flexible job shop
Collaborative heuristic
Tri-population multi-objective optimization
Double-Q network
期刊
IF:
8.5
论文数:
2.2K
被引数:
1.0W
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